Method and system to evaluate and quantify user-experience (UX) feedback

ABSTRACT

The exemplified methods and systems facilitate the evaluation of user experience feedback in a manner that is secure and private by transmitting only representation of a user&#39;s response over the network (rather than the original data files of the recording). That is, complex interaction associated with a user to a stimuli is recorded, evaluated, and condensed through a machine learning operation (specifically, a convolutional neural network) performed at the user&#39;s computing device. Indeed, only a representation of the interaction is exposed over the network when it is transmitted for subsequent action there-at. Specific audio or image recording used to evaluate the user&#39;s user-experience feedback is maintained at the user&#39;s computing device. In some embodiments, the recording is preprocessed to generate a series of matrices that can be directly fed into the machine learning operation.

RELATED APPLICATION

This application claims priority to, and the benefit of, U.S.Provisional Appl. No. 62/480,343, filed Mar. 31, 2017, entitled“Application to capture, evaluate and reward client reaction to themesplayed on a device,” which is incorporated by reference herein in itsentirety.

TECHNICAL FIELD

The present disclosure generally relates to methods and systems forevaluating and quantifying user-experience (UX) feedback in response toa user-interface presentation.

SUMMARY

The exemplified methods and systems facilitate the evaluation of userexperience feedback in a manner that is secure and private bytransmitting only representation of a user's response over the network(rather than the original data files of the recording). That is, complexinteraction associated with a user to a stimuli is recorded, evaluated,and condensed through a machine learning operation (specifically, aconvolutional neural network) performed at the user's computing device.Indeed, only a representation of the interaction is exposed over thenetwork when it is transmitted for subsequent action there-at. Specificaudio or image recording used to evaluate the user's user-experiencefeedback is maintained at the user's computing device. In someembodiments, the recording is preprocessed to generate a series ofmatrices that can be directly fed into the machine learning operation.

In some embodiments, the exemplified methods and systems facilitatecapture and evaluation of a reaction of a user to a theme played on adevice for the purpose of generating a reward to the user so as toengage the user with sponsored content.

In an aspect, a computer-implemented method is disclosed to evaluate andquantify user-experience (UX) feedback in response to a user-interfacepresentation (e.g., a themed ringtone). The method includes generating,by a processor of a computing device (e.g., smartphone, wearabledevices, smartwatches, smart glasses, laptops, desktops), via one ormore speakers of, or connected to, the computing device, a multimediaoutput of a plurality of selectable multimedia outputs accessible fromthe computing device, wherein the multimedia output comprises an audiooutput (e.g., a ringtone) associated with a stored audio file, andwherein the multimedia output is generated upon receipt of an electroniccommunication or notification (e.g., a SMS message, electronic mail) atthe computing device; recording, by the processor, via one or moremicrophones of, or connected to, the computing device, an audio streamas part of a user-experience (UX) feedback to the user-interfacepresentation, wherein the audio stream is recorded immediatelyfollowing, and/or during, the audio output being generated, and whereinthe audio stream is recorded as an audio file or as a cached data;determining, by the processor, a spectrum profile of each, or asubstantial portion, of a plurality of audio segments of the recordedaudio file or the cached data; and, determining, by the processor, amatrix comprising the plurality of spectrum profiles for each of theplurality of audio segments and providing the matrix to a machinelearning operation configured to analyze the user-experience (UX)feedback, wherein the matrix is used as an input for the machinelearning artifact configured with weights specific to the multimediaoutput to evaluate and quantify a user-experience (UX) feedback of themultimedia output.

In some embodiments, the machine learning artifact comprises aconvolutional neural network, and wherein the inputted matrix isarranged as a one-dimensional vector, a two-dimensional matrix (e.g., atwo-dimensional greyscale image), or a three-dimensional matrix.

In some embodiments, the matrix is determined by: segmenting, by theprocessor, the recorded audio file or the cached into a plurality ofsegments; normalizing, by the processor, the plurality of segments;determining, by the processor, intensity of the frequencies bands of theplurality of segments by performing a Fast Fourier Transform of theplurality of segments; and, generating, by the processor, thetwo-dimensional matrix, wherein the two-dimensional matrix is ofdimension m*n, wherein m is a number of segments of the plurality ofsegments and n is a number of frequency bands of the plurality offrequency bands, and each matrix element has a one or more scalar values(or a zero), e.g., corresponding to an intensity of a given frequencyband for a given segment.

In some embodiments, the method further includes acquiring, by theprocessor, via one or more accelerometers of the computing device, oneor more detected accelerometer signals during and/or immediatelyfollowing the audio output as an accelerometer file or as cached data,wherein the one or more detected accelerometer signals comprise anadditional part of the user-experience (UX) feedback of theuser-interface presentation; and, determining, by the processor, asecond matrix comprising the plurality of accelerometer signals andproviding the second matrix as an input to the machine learningartifact. In some embodiments, the accelerator file includesaccelerometer signals acquired for each axis of acceleration. In someembodiments, each accelerator signal is provided as an input per-axis tothe machine learning operation as an m*1 vector, which corresponds to anumber of time segments. In other embodiments, each of the accelerometersignals is decomposed to assess specific intensities of certainfrequencies in the acceleration. In some embodiments, the acceleratorinput is combined with the input matrix associated with the audio fileor audio stream.

In some embodiments, the plurality of selectable multimedia outputsfurther comprises a visualization element (e.g., ringtone includesassociated ringtone graphics), wherein the visualization element isrendered and presented on a display of the computing devicecontemporaneously with the audio output.

In some embodiments, the plurality of selectable multimedia outputsfurther comprises a haptics element (e.g., ringtone includes associatedvibration pattern), wherein the haptics element triggers rotation of amotor of the computing device to produce vibration of the computingdevice contemporaneously with the audio output.

In some embodiments, the computing device is selected from the groupconsisting of smartphone, wearable devices, smartwatches, smart glasses,laptops, and desktops.

In some embodiments, the method further includes output of the machinelearning artifact is used to assess the user-experience feedback to theuser-interface presentation based on rules specific to theuser-interface presentation.

In some embodiments, the rules to assess the user-experience feedback isbased on training data gathered from user-experience feedback to thespecific user-interface presentation.

In some embodiments, the method further includes transmitting, by theprocessor, over a network, output of the machine learning operation toan external UX analysis service, wherein the output of the machinelearning operation is used by the UX analysis service to trigger areward to an account associated with the user.

In some embodiments, the method further includes encrypting, by theprocessor, output of the machine learning artifact prior to transmissionof the output to the external UX analysis service.

In another aspect, a system is disclosed comprising a processor; and amemory having instructions stored thereon, wherein execution of theinstructions by the processor, cause the processor to: generate, via oneor more speakers of, or connected to, the system, a multimedia output ofa plurality of selectable multimedia outputs accessible to the system,wherein the multimedia output comprises an audio output (e.g., aringtone) associated with a stored audio file, and wherein themultimedia output is generated upon receipt of an electroniccommunication or notification (e.g., a SMS message, electronic mail) atthe system; record, via one or more microphones of, or connected to, thecomputing device, an audio stream as part of a user-experience (UX)feedback of the user-interface presentation, wherein the audio stream isrecorded immediately following, and/or during, the audio output beinggenerated, and wherein the audio stream is recorded as an audio file oras a cached data; determine, a spectrum profile of each, or asubstantial portion, of a plurality of audio segments of the recordedaudio file or the cached data; and, determine a matrix comprising theplurality of spectrum profiles for each of the plurality of audiosegments and provide the matrix to a machine learning artifactconfigured to analyze the user-experience (UX) feedback, wherein thematrix is used as an input for the machine learning artifact configuredwith weights specific to the multimedia output to evaluate and quantifyuser-experience (UX) feedback.

In some embodiments, the machine learning artifact comprises aconvolutional neural network, and wherein the inputted matrix isarranged as a two-dimensional matrix.

In some embodiments, the instructions, when executed by the processor,further cause the processor to: segment the recorded audio file or thecached data into a plurality of segments; normalize the plurality ofsegments; determine intensity of the frequencies bands of the pluralityof segments by performing a Fast Fourier Transform of the plurality ofsegments; and, generate the two-dimensional matrix, wherein thetwo-dimensional matrix is of dimension m*n, wherein m is a number ofsegments of the plurality of segments and n is a number of frequencybands of the plurality of frequency bands, and each matrix element hasone or more scalar values (or zero) corresponding to an intensity of agiven frequency band for a given segment.

In some embodiments, the instructions, when executed by the processor,further cause the processor to: encrypt the output of the machinelearning artifact; and, transmit, over a network, the output of themachine learning artifact to an external UX analysis service, whereinthe output of the machine learning artifact is used by the UX analysisservice to trigger a reward to an account associated with the user.

In some embodiments, the instructions when executed by the processor,further cause the processor to: acquire, via one or more accelerometersof the system, one or more detected accelerometer signals immediatelyfollowing and/or during the audio output as an accelerometer file or ascached data, wherein the one or more detected accelerometer signalscomprise an additional part of the user-experience (UX) feedback of theuser-interface presentation; and, determine a second matrix comprisingthe plurality of accelerometer signals and provide the second matrix tothe UX analysis computing device, wherein the second matrix is used as asecond input to the machine learning artifacts.

In another aspect, a non-transitory computer readable medium isdisclosed. The computer readable medium has instructions stored thereon,wherein execution of the instructions by a processor of a computingdevice causes the processor to: generate, via one or more speakers of,or connected to, the computing device, a multimedia output of aplurality of selectable multimedia outputs accessible to the computingdevice, wherein the multimedia output comprises an audio output (e.g., aringtone) associated with a stored audio file, and wherein themultimedia output is generated upon receipt of an electroniccommunication or notification (e.g., a SMS message, electronic mail) atthe computing device; record, via one or more microphones of, orconnected to, the computing device, an audio stream immediatelyfollowing, and/or during, the audio output as part of a user-experience(UX) feedback of the user-interface presentation, wherein the audiostream is recorded as an audio file or as cached data; determine, aspectrum profile of each, or a substantial portion, of a plurality ofaudio segments of the recorded audio file or the cached data; and,determine a matrix comprising the plurality of spectrum profiles foreach of the plurality of audio segments and provide the matrix to amachine learning artifact configured to analyze the user-experience (UX)feedback, wherein the matrix is used as an input for the machinelearning artifact configured with weights specific to the multimediaoutput to evaluate and quantify user-experience (UX) feedback.

In some embodiments, the machine learning artifact comprises aconvolutional neural network, and wherein the inputted matrix isarranged as a two-dimensional greyscale image.

In some embodiments, the instructions, when executed by the processor,further cause the processor to: segment the recorded audio file or thecached into a plurality of segments; normalize the plurality ofsegments; determine intensity of the frequencies bands of the pluralityof segments by performing a Fast Fourier Transform of the plurality ofsegments; and, generate a two-dimensional image having m*n pixels,wherein m is a number of segments of the plurality of segments and n isan intensity of a given frequency band.

In some embodiments, the instructions, when executed by the processor,further cause the processor to: encrypt output of the machine learningartifact; and, transmit, over a network, output of the machine learningartifact to an external UX analysis service, wherein the output of themachine learning artifact is used by the UX analysis service to triggera reward to an account associated with the user.

In some embodiments, the instructions when executed by the processor,further cause the processor to: acquire, via one or more accelerometersof the computing device, one or more detected accelerometer signalsimmediately following the audio output as an accelerometer file or ascached data, wherein the one or more detected accelerometer signalscomprise an additional part of the user-experience (UX) feedback of theuser-interface presentation; and, determine a second matrix comprisingthe plurality of acquired accelerometer signal files or cached data andproviding the second matrix to the machine learning artifact, whereinthe second matrix is used as a second input for the machine learningartifact configured with weights specific to the multimedia output toevaluate and quantify user-experience (UX) feedback.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention may be better understood from thefollowing detailed description when read in conjunction with theaccompanying drawings. Such embodiments, which are for illustrativepurposes only, depict novel and non-obvious aspects of the invention.The drawings include the following figures:

FIG. 1 is a flow diagram of a method to evaluate and quantifyuser-experience (UX) feedback in response to a user-interfacepresentation (e.g., a themed ringtone) in accordance with anillustrative embodiment.

FIG. 2 shows a detailed flow diagram of the method of FIG. 1 to evaluateand quantify user-experience (UX) feedback in response to auser-interface presentation (e.g., a themed ringtone) in accordance withan illustrative embodiment.

FIG. 3 shows a diagram of a system to evaluate and quantifyuser-experience (UX) feedback in response to a user-interfacepresentation in accordance with an illustrative embodiment.

FIG. 4 shows a diagram of a ringtone database in accordance with anillustrative embodiment.

FIG. 5 shows a diagram of a user database in accordance with anillustrative embodiment.

FIG. 6 shows a diagram of a response database in accordance with anillustrative embodiment.

FIG. 7 shows an example graphical user interface of a ringtone selectionscreen in accordance with an illustrative embodiment.

FIG. 8 shows an example graphical user interface of a ringtone selectionconfirmation screen in accordance with an illustrative embodiment.

FIG. 9 shows an example notification that is transmitted to the user'scomputing device upon a positive assessment of a user-experience (UX)feedback being determined in accordance with an illustrative embodiment.

FIG. 10 is a flow diagram of a method to setup a ringtone in accordancewith an illustrative embodiment.

FIG. 11 is a system diagram of selecting a user-interface presentation(e.g., a ringtone) according to the method of FIG. 10 in accordance withan illustrative embodiment.

DETAILED DESCRIPTION

Each and every feature described herein, and each and every combinationof two or more of such features, is included within the scope of thepresent invention provided that the features included in such acombination are not mutually inconsistent.

It is understood that throughout this specification the identifiers“first”, “second”, “third”, “fourth”, “fifth”, “sixth”, and such, areused solely to aid in distinguishing the various components and steps ofthe disclosed subject matter. The identifiers “first”, “second”,“third”, “fourth”, “fifth”, “sixth”, and such, are not intended to implyany particular order, sequence, amount, preference, or importance to thecomponents or steps modified by these terms.

FIG. 1 is a flow diagram of a method 100 to evaluate and quantifyuser-experience (UX) feedback in response to a user-interfacepresentation (e.g., a themed ringtone) in accordance with anillustrative embodiment. FIG. 2 shows a detailed flow diagram of themethod 100 of FIG. 1 to evaluate and quantify user-experience (UX)feedback in response to a user-interface presentation (e.g., a themedringtone) in accordance with an illustrative embodiment.

In FIG. 1, the method 100 includes generating (step 102), by a processorof a computing device (e.g., smartphone, wearable devices, smartwatches,smart glasses, laptops, desktops), via one or more speakers of, orconnected to, the computing device, a multimedia output of a pluralityof selectable multimedia outputs accessible to the computing device inwhich the multimedia output includes an audio output (e.g., a ringtone)associated with a stored audio file and is generated upon receipt of anelectronic communication or notification (e.g., a SMS message,electronic mail) at the computing device. As further shown in FIG. 2, insome embodiments, upon a message (e.g., SMS message) being received(step 202) at the computing device, a background service 300 operatingon the computing device detects (step 204) the received SMS message andtriggers a status bar notification (step 206) of the received SMSmessage. In some embodiments, the status bar notification includes anidentifier of the sender and a portion of the message.

In parallel, before, or following the notification, the backgroundservice 304 is configured to check (step 208) the system setting andnotification preference to determine whether the computing device is setto silent. When the system setting is not set to silent, the computingdevice is configured to generate (step 210), via one more speakers ofthe computing device, a multimedia output corresponding to a ringtone.When the system setting is set to silent, no further action is takenwith respect to the evaluation of the user's UX response.

In some embodiments, in addition to audio output, the plurality ofselectable multimedia outputs further includes a visualization element(e.g., ringtone includes associated ringtone graphics) or a hapticselement. In some embodiments, the visualization element is rendered andpresented on a display of the computing device contemporaneously withthe audio output. In some embodiments, the haptics element is anassociated vibration pattern associated with a motor that produces thevibration of the computing device contemporaneously with the audiooutput.

Referring back to FIG. 1, the method 100 then includes recording (step104), by the processor, via one or more microphones of, or connected to,the computing device, an audio stream as part of a user-experience (UX)feedback of the user-interface presentation. The audio stream isrecorded immediately following, and/or during, the audio output beinggenerated. The audio stream is recorded as an audio file or as cacheddata. As used herein, the term “cache” is used interchangeably with theterm “buffer”. As further shown in FIG. 2, the background service (e.g.,310) is configured to enable (step 211) the recording module of theaudio stream. In some embodiments, the background service (e.g., 310)directs the computing device to record a pre-defined length audio files,e.g., for at least about 0.3 seconds. In some embodiments, the recordedlength is longer, e.g., for at least about 0.5 seconds, for at leastabout 1.0 seconds, for at least about 1.5 seconds, or for at least about2.0 seconds. In some embodiments, the recorded length is greater than2.0 seconds. In some embodiments, the recorded length is dynamicallydetermined and is performed for the duration that an audio input issensed up to a maximum duration (e.g., up to about 1 second or up toabout 10 seconds). When the system setting is set to silent, thebackground service takes no action of recording or processing a responseof the user. As noted, the recording can initiate during the generationof the audio output. In some embodiments, the recording can initiateimmediately after the recording, which can be with a millisecond or afraction of a second (e.g., 0.1 seconds) following generation of theaudio output.

In some embodiments, the recorded audio stream is saved to a memorycache of buffer of the computing device. In some embodiments, therecorded audio stream is saved to as an audio file.

The method 100 then includes determining (step 106), by the processor, aspectrum profile of each, or a substantial portion, of a plurality ofaudio segments of the recorded audio file or the cached data. A spectrumprofile refers to a set of frequency-related components of an audio fileor stream, such as ranges and distributions, that are decomposed fromthe audio file or stream during the pre-processing operation. Thedistribution can be tiered into bands. As further shown in FIG. 2, insome embodiments, the background service is configured to pass (step212) the stored audio stream in cache/buffer, or the audio file, to apreprocessor module configured to segment the audio stream or file intoequivalent pieces (e.g., 20 milli-second snippets). Other duration maybe used to segment the audio stream or file.

The method 100 then includes determining (step 110) a matrix comprisingthe plurality of spectrum profiles for each of the plurality of audiosegments and provide the matrix to a machine learning operationconfigured to analyze the user-experience (UX) feedback in which thematrix is used as an input to a machine learning operation configuredwith weights specific to the multimedia output to evaluate and quantifya user-experience (UX) feedback to the multimedia output. As furthershown in FIG. 2 (step 214), in some embodiments, the segmented audiostream segments or file segments are normalized (e.g., by scaling eachof the value to a maximum value in the audio stream segment of filesegment) and a frequency profile of each segment is evaluated. In someembodiments, the frequency profile is generated based on a Fast FourierTransform (FFT) operation that converts the time-series data set to afrequency data set to which main frequency components of the segment isdetermined. In some embodiments, the result (of step 214) is atwo-dimensional matrix in which a first dimension includes a quantity ofthe audio segments and the second dimension includes a quantity of thefrequency bands for that segment. In some embodiments, thetwo-dimensional matrix is generated as a two-dimensional greyscale imagehaving m*n pixels. A FFT operation can include a short-term FourierTransform or any decomposition or reconstruction operation thatdecompose the input signal into its frequency or energy components.

As a non-limiting example, in some embodiments, a convolutional neuralnetwork (operating on the computing device) takes as input atwo-dimensional matrix formed as a greyscale image with m*n pixels inwhich each pixel has a single scalar value corresponding to its energyor intensity within a set of frequency bands. To generate the m*n pixelsfrom the one-dimensional audio file or data so it can be analyzed by theconvolutional neural network, the audio file or data is segmented intosegments of a pre-defined length, e.g., 20 milliseconds. Each of audiosnippets of 20-ms lengths is normalized, in some embodiments, forexample, via a blend average operation (e.g., as noted above, by scalingeach of the value to a maximum value in the audio stream segment of filesegment) of the intensity of the frequencies bands. That is, eachsegments are evaluated to determine a frequency distribution which aretiered or placed in buckets. In some embodiments, the distribution isbased on 20-Hz increments, and is generated via a Fast FourierTransform. To this end, a 2D image is created with m*n pixels in which mis a number of 20-ms snippets that is created within the total length ofthe audio file or data, and n is a number of 20-Hz frequency bands(i.e., buckets). The pixel value of each of m*n pixel is then theintensity (e.g., energy) of the frequency component of a given snippetwithin a frequency band.

For example, for a 3-second input, the background service can partitionthe recorded audio file or data into 150 segments, each having a lengthof 20 milliseconds (i.e., m=60). A Fast Fourier Transform of each of the20-ms segments is performed (by the background service) to convert thetime-series data into the frequency domain in which each frequency isarranged in 20-Hz increments. For a frequency range of 4 KHz, n=200.Thus, a 150*200 matrix is generated from the 3-second input.

For other recorded durations, the image size can be expressed as: (# ofseconds)*(50 segments per second)*(200 frequency ranges per segment)with these configurations. Of course, the image size can vary with adifferent degree of segmentation or FFT window size. As noted herein,the matrix can be configured with other sizes and shapes.

Other machine learning operations or algorithms may be used which areconfigured to receive as input a 1D vectors or tensors of higherdimension.

In some embodiments, the matrix is processed (step 214) in an inputlayer for a convolutional neural network (CNN) locally executing on thecomputing device in which the neural network weights of theconvolutional neural network are ringtone specific. That is, the weightsare established based on trained data set associated with a set oftraining user responses. In some embodiments, the training responsesinclude voice recording having specific inflections in voice, emphasisof certain aspects of the utterance, certain verbal expression, etc.,that are associated, for example, with excitement, happiness, etc. Insome embodiments, the training responses include voice recording ofindividuals from various gender and age groups. The output of theconvolutional neural network is a representation of the user-experiencefeedback that can be used to take subsequent action. The input for theconvolutional neural network (as a machine learning operation), in someembodiments, is a two-dimensional matrix (e.g., a 2D greyscale image).In other embodiments, the input is a single dimensional matrix, athree-dimensional matrix, etc., or a combination thereof. The weights ofthe machine learning algorithm for a given audio output can be refinedover time based on additional training data

In some embodiments, the trained model is used to assess a positiveuser-experience of the user-interface presentation. In some embodiments,the output of the machine learning operation is used to refine thetrained model to be used for the instant and other users.

Indeed, this representation of the audio file or data (i.e., output ofthe convolutional neural network) condenses the evaluation of the userexperience feedback, making it more efficient and straightforward forsubsequent analysis (e.g., in assessing whether the user's response waspositive, whether the user response indicates that the user was excitedor happy, etc.). Further, in addition to be more efficient in data size,the representation addresses issue of user privacy as only arepresentation of the response, and not the response itself (e.g., therecorded audio), is being provided over a network to an externalprocessor (e.g., a UX analysis computing device).

Referring back to FIG. 1, the method 100 includes transmitting (step110) the output of the machine learning operation to an external serverover a network to which action based on the determined user-experiencedfeedback can be performed. As further shown in FIG. 2, in someembodiments, the background service encrypts (step 216) the output ofthe convolutional neural network by a symmetric key that is specific tothe user and is known to the external processor. The encrypted datastream of file is then stored (step 216) in cache/buffer or in thedevice memory.

The background service then uploads (step 218) the cached/buffered fileor data stream to a server associated with the external processor, whichdecrypts the uploaded file using a private key queried from a database(e.g., user DB) associated with the user. The decrypted representationis saved (step 220) on a server and is subsequently evaluated, e.g., todetermine the user response (e.g., excited, happy, etc).

This encryption ensures that a unique instance of the user-experiencefeedback is sent and operated upon by the external processor. In someembodiments, the encryption key is updated periodically to reducelikelihood of manipulation of the system.

In some embodiments, the representation of user-experience feedback(e.g., output of the machine learning artifact) is used by the externalprocessor (e.g., a UX analysis service) to trigger a reward to anaccount associated with the user. As shown in FIG. 2, in someembodiments, a URI (uniform resource identifier) identifying theencrypted output layer of the convolutional neural network is passed(step 222) to a server (e.g., associated with a response DB) along withthe user's username and an identifier of the selected multimedia output(e.g., ringtone ID). The decrypted output layer of the convolutionalneural network is passed (step 224) to a response evaluation operationwhich evaluates the output layer to determine if the response wascorrect (e.g., corresponding to, associated with, and/or correlated withthe training data used) and information which might be used to refinethe targetable profile of the user and about the emotional state of theuser. If the response was correct, a notification is created (step 226)informing the user about the reward and a balance associated with theuser in the database (e.g., user DB) is incremented by the amount of thereward.

In some embodiments, the method 100 further includes using other sensorsof the computing device to evaluate and quantify user-experience (UX)feedback in response to a user-interface presentation. In someembodiments, the method 100 includes using the device's accelerometer.In some embodiments, during the recording of the audio stream, method100 further includes acquiring, by the processor, via one or moreaccelerometers of, or connected to, the computing device, one or moredetected accelerometer signals immediately following or during the audiooutput as an accelerometer file or as cached/buffered data, wherein theone or more detected accelerometer signals comprise an additional partof the user-experience (UX) feedback of the user-interface presentation.In some embodiments, a second matrix (e.g., two-dimensional image) isgenerated comprising the plurality of spectrum profiles of the pluralityof accelerometer signal segments for each of the plurality ofaccelerometer signal segments. Indeed, the convolutional neural networkis such embodiments is configured to receive more than one greyscaleimages or matrices.

In some embodiments, the accelerometer signal is a per-axis accelerationacquired from multiple single-channel accelerometers or one moremulti-axis accelerometers over time. Each accelerometer signal, in someembodiments, is not decomposed into frequency bands and is directly fedinto the machine learning operation as a “k*1” vector (in which k is thenumber of time segments). In some embodiments, for a 6-axisaccelerometer (e.g., having 3 longitudinal and 3 rotational axis), theaccelerometer inputs are clustered as a “k*6” matrix.

In some embodiments, the accelerometer signals are combined (e.g.,appended) to the input matrix of the audio input to which correspondingtime segments are aligned (e.g., to form a combined matrix of dimensionk*(n+6)).

In some embodiments, the accelerometer data is decomposed to determineintensities or energy of the signal for specific bands of frequencies.

Exemplary System

FIG. 3 shows a diagram of a system 300 to evaluate and quantifyuser-experience (UX) feedback in response to a user-interfacepresentation in accordance with an illustrative embodiment. In FIG. 3,the computing device 302 is shown executing a background service 304. Insome embodiments, the background service 304 is an executable file(e.g., an APP). Background services can include executable instructions,libraries, parse-able code, and any invoke-able services on a computingdevice. The background service 304 operatively communicate with themachine learning operation (shown as machine learning network 306). Themachine learning operation 306 receives a machine-learning-based trainedmodel 308 through the system services 310 (e.g., network interface) whenthe selectable multimedia outputs are installed to the system. Thesystem services 310 further provide system configuration and settings312 to the background service 304. The background service 304 isconfigured to react to an incoming message 314 received from an externalmessage server or network 316.

As further shown in FIG. 3, the output 318 of the machine learningoperation 306 is operatively coupled to an encryption module 320. Theencryption module 320 generates an encrypted output 322 of the machinelearning operation 306 which is transmitted over a network tocorresponding decryption module 324 located at the UX analysis computingdevice. The UX analysis computing device may include more than oneserver. In some embodiments, the UX analysis computing device includes astorage area network that maintains a user database (shown as “User DB”326) and a response database (shown as “Response DB” 328). The userdatabase 326 maintains, among other things, encryption keys (shown as330 a and 330 b) for each of the users that is accessible to the UXanalysis computing device and to the user's computing device 302. Aninstance of the user database 326 specific to the user is stored on thedevice 302 and is synchronized with that of the server. Indeed, thedevice 302 has access only to the information of user associated withthe computing device including user name, profile information,authentication such as the user's email address, and the user's rewardbalance. The encryption key is not accessible by the user or otherapplications on the device.

Referring still to FIG. 3, the output 332 of the decryption module 324,as the decrypted output layer of the machine-learned operation, isprovided to storage 334 and to a response evaluator module 336. Theresponse evaluator module 336 is configured to evaluate the output layer332 of the machine-learned operation to classify the outputs either as apositive response or a negative response to which a reward should beprovided. The output of the response evaluator 336 is provided as aresponse evaluation 338 to the response database 328.

The response database 328 stores information relating to the userexperience feedback of a user to a given stimuli (e.g., user's verbaland/or physical response to the ringtone). In some embodiments, theinformation includes the ML output layer data 332, the time and locationof the response, the URI of the representation 340 of the audio filefrom the recorded response, and information 338 gathered on the responsesuch as whether the response was positive, whether a reward was grantedfor the response, and/or the reward amount or type granted for theresponse.

When a new entry is entered in the response database, the balance of thecorresponding user is increased (shown as “reward” 342) and anotification (344) is sent through a user interface module 346. Examplesof the notifications includes a cheering message, a coupon, a monetaryor credit, or access to a special multimedia content. The reward mightbe send to the user for each reaction, and bundled for various reactionsbased on a logic.

The UX analysis computing device further includes a user evaluatormodule 348. The user evaluator module 348 retrieves the user responseinformation 350 from the response database 328. The targetable profileis updated based on information gathered by responses and selection ofringtones.

FIG. 4 shows a diagram of a ringtone database 402 in accordance with anillustrative embodiment. The ringtone database 402 may includeinformation about the users and the ringtone that may be targeted to agiven user. As shown in FIG. 4, the ringtone database 402 may include anidentifier field 404, a ringtone title field 406, a ringtone descriptionfield 408, a ringtone category field 410, an audio URI field 412, animage URI field 414, a model URI field 416, a reward profile field 418,a target group field 420, and a target location field 422.

As noted above, a partial instance of the ringtone DB can be downloadedto the computing device 302 when a ringtone selection is opened.Information directed to title 406, description 408, reward amount 418,and category 410 may be displayed at the user's computing device, e.g.,in a ringtone selection screen 702 (not shown, see FIG. 7). Acorresponding image of each ringtone can be downloaded from the URIspecified in the image URI field 414. When a new ringtone is selected atthe user's computing device 302 in the ringtone selection activityscreen, a ringtone audio file can be downloaded from the URI specifiedin the Audio URI field 412, and the trained neural network with theringtone specific weights can be downloaded from the URI specified inthe model URI field 416.

FIG. 5 shows a diagram of a user database 326 in accordance with anillustrative embodiment. An instance of the user database 326 can bestored on the user's computing device 302 and synchronized with the UXanalysis computing device. As shown in FIG. 5, the user database 326 mayinclude a username field 424, a provider authentication field 426, alocation field 428, a demographic field 430, a targetable profile field432, a balance field 434, an encrypted key field 436, and a selectedringtone field 437.

The user's computing device 302, in some embodiments, has access only toinformation associated with the user including the user's username,user's profile information, user's authentication information such as anemail address associated with the user's account, and a user's balance.The encryption key 330 a is configured by the system not to beaccessible by the user. The encryption key 330 a can be updatedperiodically to improve fraud protection. The encryption key 330 a canbe updated in the APP by a remote maintenance service or module. Theencryption key 330 a may be stored in a secure memory space.

Referring still to FIG. 5, the information stored in the targetableprofile field 432 may be used to determine which ringtones areaccessible to the user. The targetable profile field 432 may be updatedbased on information gathered by responses (e.g., 350) and selection ofringtones.

FIG. 6 shows a diagram of a response database 328 in accordance with anillustrative embodiment.

The response database 328 is not accessible by the user's computingdevice 302. It stores information on responses to ringtones such as theuser responding to the ringtone, time and location of the response, theURI of the representation of the audio file from the recorded response,information gathered on response such as whether a response was positiveand whether a reward was granted for the response. When a new entry isentered in the response database, the balance of the corresponding useris increased. As shown in FIG. 6, the response database 328 may includea username field 438, a date and time field 440, a location field 442, aresponse URI field 444, a ringtone identifier field 446, a mood field448, an energy field 450, a company field 452, and a reward field 454.

FIG. 7 shows an example graphical user interface of a ringtone selectionscreen 702 in accordance with an illustrative embodiment. As shown inFIG. 7, the ringtone selection screen 702 includes three selectableringtones 704 a, 704 b, 704 c that are presented to the user on theuser's computing device 302. Each of the selectable ringtones include animage 706 associated with the ringtone, a title 708 for the ringtone, adescription of the ringtone 710, and a reward amount 712. The rewardamount 712 is the amount of reward that would be credited to a user'saccount upon a positive reaction being assessed based on a ringtonebeing presented and/or played to the user in response to a SMS message.

FIG. 8 shows an example graphical user interface of a ringtone selectionconfirmation screen 802 in accordance with an illustrative embodiment.The screen 802 includes an input widget 804 to play a sample of theringtone, an input widget 806 to accept the ringtone, and an inputwidget 808 (shown as “cancel”) to return to the selection screen 702.

FIG. 9 shows an example notification 344 that is transmitted to theuser's computing device 302 upon a positive assessment of auser-experience feedback being determined in accordance with anillustrative embodiment.

FIG. 10 is a flow diagram of a method 1000 to setup a ringtone inaccordance with an illustrative embodiment. FIG. 11 is a system diagramof selecting a user-interface presentation (e.g., a ringtone) accordingto the method of FIG. 10 in accordance with an illustrative embodiment.

The method 1000 includes, upon launching (step 1002) the setup activity1100 of an APP on the user's computing device 302, querying (step 1004)the ringtone database 402 for available ringtones 1102. The servers ofthe UX analysis computing devices provides available ringtones 1102 tothe ringtone selection screen 702 presented at the user's computingdevice 302. The APP is configured to query (step 1006) the user database326 for the current ringtone ID 1104 and display (step 1008) availableringtones in ringtone selection screen 702. The APP further downloadsany missing logo image files 706 to the cache 1106 (and/or buffer) iffile is not available in either cache 1106 or device persistent storage1108. Upon the user selecting a ringtone selection in the ringtoneselection screen 702, the APP presents (step 1010) the user with aringtone selection confirmation screen 802. By clicking (step 1012) onthe play button 804, a ringtone is played to the user. The APP isconfigured to download the ringtone audio file to the cache/buffer ifthe file is not available in either cache/buffer or device persistentstorage. By clicking the OK button 806, the user selects (step 1014) thecurrent ringtone in the detail view as the new ringtone. The APP thensaves (step 1016) the audio file of the current ringtone to persistentmemory on the computing device 302. The APP then downloads (step 1018)the machine-learning-based trained model 308 for the machine learningoperation 306 to persistent memory 1108 on the device 302. The APP thensaves (step 1020) a local URI of the audio file 1110 to the settings1112 of the ringtone and set the local URI of the machine-learning-basedtrained model 308 to the settings 1112. The APP then set (step 1022) theringtone identifier at the user database 326.

Indeed, the exemplified methods and system facilitates the capture,evaluation, and reward of a user's reaction to a theme which is playedon the user's computing device. Besides ringtone, other interactions andreactions to other types of themes can be assessed using the exemplarymethods and systems described herein. The exemplified systems andmethods can be applied to any interaction from the client devices to theclient to make the client aware of an intended target notification orinformation and to provoke a client reaction to a theme. The exemplifiedsystems and methods can be used to further evaluate a user's reaction orresponse to receipt of a reward. In some embodiments, in addition or insubstitution of a reward, the APP can be configured to play anothertheme or multimedia output upon sensing a positive assessment of theuser experience feedback.

The trigger to play a theme might be an incoming message, call,notification from an application having access to at least one of theclient devices or being installed on at least one client device, areward for the reaction on a theme, or a trigger from the client whichcan be sensed by at least one of the client devices.

The theme might be any combination of visual, acoustic and hapticsignals.

Examples for visual signals are blinking flash light, text on thescreen, a video, the picture captured by the device camera, streamingcontent, and reality augmented content. Examples for acoustic signalsare songs, rhythms, melodies, and slogans. Examples for haptic signalsare rotation, vibration and texture sensible on the surface of a clientdevice. The theme might be played on various client devices at the sametime. An example for a theme being played on more than one client deviceis when the cell phone plays a melody while the smart watch vibrates andblinks.

Visual, acoustic, tactile and motion signals might be captured to recordthe client reaction to a theme. Examples for sensors to detect visualsignals of the reaction are illumination sensors and cameras. Examplesfor sensors detecting devices include cellphones, wearables, IoT devicesand computers. Examples for cellphones are smartphones and flip phones.Examples for wearables are smart watches, fitness trackers, headsets,smart glasses and virtual reality devices. Examples for IoT devices aresmart TVs, refrigerators, thermostats, and other devices which areconnected to a network and which are able to emit and/or receiveinformation. Examples for computers are tablets, laptops, personalcomputers and game consoles.

There are various possible reactions to a theme. Examples for how toreact to a theme are to respond, complete, accompany, mimic, make faces,and pose.

Various criteria might be taken into account to evaluate the reaction.Examples for reactions are if and which reaction was captured, clientemotion in the response, the environment where the reaction was capturedand the company of the client during the caption of the reaction. Theclient expresses his emotion in the reaction to the theme verbally andnon-verbally.

Examples for how the emotion is expressed non-verbally in the reactionare the facial expression, gestures, biosignals and identification.Examples for biosignals are to turn red, sweat, give goose skin, andtears rolling. An examples for identification is to place an objectbetween the upper lip and nose to play having a mustache.

Examples for information on the environment where the reaction wascaptured are illumination, background acoustics, temperature, andlocation. Examples for how the explicit location could be determined areif the client device shares the location with the application, and ifthe client device is detected by a local network of which the locationis known. Examples for local networks are Wi-Fi, Bluetooth, near-field,and cellular.

As used herein, processor refers to a physical hardware device thatexecutes encoded instructions for performing functions on inputs andcreating outputs. Exemplary processors for use in this disclosure aredescribed herein in relation to FIGS. 1-5. In some embodiments, theprocessor may comprise a plurality of processors that are incommunication with one another. Processors can include microprocessors,graphic-based processing units (GPUs), ASICs, microcontrollers, andquantum processors that can execute instructions.

As used herein, “computer” may include a plurality of computers. Thecomputers may include one or more hardware components such as, forexample, a processor, a random access memory (RAM) module, a read-onlymemory (ROM) module, a storage, a database, one or more input/output(I/O) devices, and an interface. Alternatively and/or additionally,computer may include one or more software components such as, forexample, a computer-readable medium including computer executableinstructions for performing a method associated with the exemplaryembodiments. It is contemplated that one or more of the hardwarecomponents listed above may be implemented using software. For example,storage may include a software partition associated with one or moreother hardware components. It is understood that the components listedabove are exemplary only and not intended to be limiting.

Processor may include one or more processors, each configured to executeinstructions and process data to perform one or more functionsassociated with a computer for indexing images. Processor may becommunicatively coupled to RAM, ROM, storage, database, I/O devices, andinterface. Processor may be configured to execute sequences of computerprogram instructions to perform various processes. The computer programinstructions may be loaded into RAM for execution by processor.

RAM and ROM may each include one or more devices for storing informationassociated with operation of processor. For example, ROM may include amemory device configured to access and store information associated withthe computer including information for identifying, initializing, andmonitoring the operation of one or more components and subsystems. RAMmay include a memory device for storing data associated with one or moreoperations of processor. For example, ROM may load instructions into RAMfor execution by processor.

Storage may include any type of mass storage device, includingnetwork-based storage, configured to store information that processormay need to perform processes consistent with the disclosed embodiments.For example, storage may include one or more magnetic and/or opticaldisk devices, such as hard drives, CD-ROMs, DVD-ROMs, or any other typeof mass media device.

Database may include one or more software and/or hardware componentsthat cooperate to store, organize, sort, filter, and/or arrange dataused by the computer and/or processor. For example, database may storethe source CAD model and parameters to generate the three-dimensionalmeta-structure models therefrom. It is contemplated that database maystore additional and/or different information than that listed above.

I/O devices may include one or more components configured to communicateinformation with a user associated with computer. For example, I/Odevices may include a console with an integrated keyboard and mouse toallow a user to maintain a database of images, update associations, andaccess digital content. I/O devices may also include a display includinga graphical user interface (GUI) for outputting information on amonitor. I/O devices may also include peripheral devices such as, forexample, a printer for printing information associated with controller,a user-accessible disk drive (e.g., a USB port, a floppy, CD-ROM, orDVD-ROM drive, etc.) to allow a user to input data stored on a portablemedia device, a microphone, a speaker system, or any other suitable typeof interface device.

Interface may include one or more components configured to transmit andreceive data via a communication network, such as the Internet, a localarea network, a workstation peer-to-peer network, a direct link network,a wireless network, or any other suitable communication platform. Forexample, interface may include one or more modulators, demodulators,multiplexers, demultiplexers, network communication devices, wirelessdevices, antennas, modems, and any other type of device configured toenable data communication via a communication network.

Unless otherwise expressly stated, it is in no way intended that anymethod set forth herein be construed as requiring that its steps beperformed in a specific order. Accordingly, where a method claim doesnot actually recite an order to be followed by its steps or it is nototherwise specifically stated in the claims or descriptions that thesteps are to be limited to a specific order, it is no way intended thatan order be inferred, in any respect.

While the methods and systems have been described in connection withcertain embodiments and specific examples, it is not intended that thescope be limited to the particular embodiments set forth, as theembodiments herein are intended in all respects to be illustrativerather than restrictive.

What is claimed is:
 1. A computer-implemented method to evaluate andquantify user-experience (UX) feedback in response to a user-interfacepresentation (e.g., a themed ringtone), the method comprising:generating, by a processor of a computing device, via one or morespeakers of, or connected to, the computing device, a multimedia outputof a plurality of selectable multimedia outputs accessible from thecomputing device, wherein the multimedia output comprises an audiooutput associated with a stored audio file, and wherein the multimediaoutput is generated upon receipt of an electronic communication ornotification at the computing device; recording, by the processor, viaone or more microphones of, or connected to, the computing device, anaudio stream as part of a user-experience (UX) feedback to theuser-interface presentation, wherein the audio stream is recordedimmediately following, and/or during, the audio output being generated,and wherein the audio stream is recorded as an audio file or as a cacheddata; determining, by the processor, a spectrum profile of each, or asubstantial portion, of a plurality of audio segments of the recordedaudio file or the cached data; and determining, by the processor, amatrix comprising the plurality of spectrum profiles for each of theplurality of audio segments and providing the matrix to a machinelearning operation configured to analyze the user-experience (UX)feedback, wherein the matrix is used as an input for the machinelearning operation configured with weights specific to the multimediaoutput to evaluate and quantify user-experience (UX) feedback of themultimedia output.
 2. The method of claim 1, wherein the machinelearning operation comprises a convolutional neural network, and whereinthe inputted matrix is arranged as a two-dimensional matrix.
 3. Themethod of claim 2, wherein the inputted matrix is determined by:segmenting, by the processor, the recorded audio file or the cached intoa plurality of segments; normalizing, by the processor, the plurality ofsegments; determining, by the processor, intensity of frequencies bandsof the plurality of segments by dividing into frequency components ofthe plurality of segments; and generating, by the processor, thetwo-dimensional matrix, wherein the two-dimensional matrix is ofdimension m*n, wherein m is a number of segments of the plurality ofsegments and n is a number of frequency bands of a plurality offrequency bands to which the segment is evaluated, and wherein eachmatrix element has at least a scalar value corresponding to an intensityof a given frequency band for a given segment.
 4. The method of claim 1,further comprising: acquiring, by the processor, via one or moreaccelerometers of the computing device, one or more detectedaccelerometer signals immediately following the audio output as anaccelerometer file or as cached data, wherein the one or more detectedaccelerometer signals comprise an additional part of the user-experience(UX) feedback of the user-interface presentation; and determining by theprocessor, a second matrix comprising the plurality of accelerometersignals and providing the second matrix as an input to the machinelearning operation.
 5. The method of claim 1, wherein the plurality ofselectable multimedia outputs further comprises a visualization element(e.g., ringtone includes associated ringtone graphics), wherein thevisualization element is rendered and presented on a display of thecomputing device contemporaneously with the audio output.
 6. The methodof claim 5, wherein the plurality of selectable multimedia outputsfurther comprises a haptics element (e.g., ringtone includes associatedvibration pattern), wherein the haptics element triggers rotation of amotor of the computing device to produce vibration of the computingdevice contemporaneously with the audio output.
 7. The method of claim1, wherein the computing device is selected from the group consisting ofsmartphone, wearable devices, smartwatches, smart glasses, laptops, anddesktops.
 8. The method of claim 1, further comprising: wherein theoutput of the machine learning operation is used to assess a positiveuser-experience and/or a negative user-experience of the user-interfacepresentation.
 9. The method of claim 1, further comprising:transmitting, by the processor, over a network, output of the machinelearning operation to an external UX-analysis service, wherein theoutput of the machine learning operation is used by the UX-analysisservice to trigger a reward to an account associated with the user. 10.The method of claim 9, further comprising: encrypting, by the processor,output of the machine learning operation prior to transmission of theoutput to the external UX-analysis service.
 11. A system comprising: aprocessor; and a memory having instructions stored thereon, whereinexecution of the instructions by the processor, cause the processor to:generate, via one or more speakers of, or connected to, the system, amultimedia output of a plurality of selectable multimedia outputsaccessible to the system, wherein the multimedia output comprises anaudio output (e.g., a ringtone) associated with a stored audio file, andwherein the multimedia output is generated upon receipt of an electroniccommunication or notification (e.g., a SMS message, electronic mail) atthe system; record, via one or more microphones of, or connected to, thesystem, an audio stream as part of a user-experience (UX) feedback of auser-interface presentation, wherein the audio stream is recordedimmediately following, and/or during, the audio output being generated,and wherein the audio stream is recorded as an audio file or as a cacheddata; determine, a spectrum profile of each, or a substantial portion,of a plurality of audio segments of the recorded audio file or thecached data; and determine a matrix comprising the plurality of spectrumprofiles for each of the plurality of audio segments and provide thematrix to a machine learning artifact configured to analyze theuser-experience (UX) feedback, wherein the matrix is used as an input tothe machine learning artifact configured with weights specific to themultimedia output to evaluate and quantify a user-experience (UX)feedback of the multimedia output.
 12. The system of claim 11, whereinthe machine learning artifact comprises a convolutional neural network,and wherein the inputted matrix is arranged as a two-dimensional matrix.13. The system of claim 12, wherein the instructions, when executed bythe processor, further cause the processor to: segment the recordedaudio file or the cached into a plurality of segments; normalize theplurality of segments; and determine intensity of the frequencies bandsof the plurality of segments by dividing into frequency components ofthe plurality of segments; and generate the two-dimensional matrix,wherein the two-dimensional matrix is of dimension m*n, wherein m is anumber of segments of the plurality of segments and n is a number offrequency bands of the plurality of frequency bands, and each matrixelement has at least a scalar value corresponding to an intensity of agiven frequency band for a given segment.
 14. The system of claim 11,wherein the instructions, when executed by the processor, further causethe processor to: encrypt output of the machine learning artifact; andtransmit, over a network, output of the machine learning artifact to anexternal UX-analysis service, wherein the output of the machine learningartifact is used by the external UX-analysis service to trigger a rewardto an account associated with the user.
 15. The system of claim 11,wherein the instructions when executed by the processor, further causethe processor to: acquire, via one or more accelerometers of the system,one or more detected accelerometer signals immediately following orduring the audio output as an accelerometer file or as cached data,wherein the one or more detected accelerometer signals comprise anadditional part of the user-experience (UX) feedback of theuser-interface presentation; and determine a second matrix comprisingthe plurality of accelerometer signals and provide the second matrix tothe UX-analysis computing device, wherein the second matrix is used as asecond input to the machine learning artifacts.
 16. A non-transitorycomputer readable medium having instructions stored thereon, whereinexecution of the instructions by a processor of a computing devicecauses the processor to: generate, via one or more speakers of, orconnected to, the computing device, a multimedia output of a pluralityof selectable multimedia outputs accessible to the computing device,wherein the multimedia output comprises an audio output (e.g., aringtone) associated with a stored audio file, and wherein themultimedia output is generated upon receipt of an electroniccommunication or notification (e.g., a SMS message, electronic mail) atthe computing device; record, via one or more microphones of, orconnected to, the computing device, an audio stream immediatelyfollowing, and/or during, the audio output as part of a user-experience(UX) feedback of the user-interface presentation, wherein the audiostream is recorded as an audio file or as a cached data; determine, aspectrum profile of each, or a substantial portion, of a plurality ofaudio segments of the recorded audio file or the cached data; anddetermine a matrix comprising the plurality of spectrum profiles foreach of the plurality of audio segments and provide the matrix to amachine learning artifact configured to analyze the user-experience (UX)feedback, wherein the matrix is used as an input to a machine learningartifact configured with weights specific to the multimedia output toevaluate and quantify a user-experience (UX) feedback to the multimediaoutput.
 17. The computer readable medium of claim 16, wherein themachine learning artifact comprises a convolutional neural network, andwherein the inputted matrix is arranged as a two-dimensional matrix. 18.The computer readable medium of claim 16, wherein the instructions, whenexecuted by the processor, further cause the processor to: segment therecorded audio file or the cached data into a plurality of segments;normalize the plurality of segments; and determine intensity of thefrequencies bands of the plurality of segments by dividing intofrequency components of the plurality of segments; and generate thetwo-dimensional matrix, wherein the two-dimensional matrix is ofdimension m*n, wherein m is a number of segments of the plurality ofsegments and n is a number of frequency bands of the plurality offrequency bands, and each matrix element has at least a scalar valuecorresponding to an intensity of a given frequency band for a givensegment.
 19. The computer readable medium of claim 16, wherein theinstructions, when executed by the processor, further cause theprocessor to: encrypt output of the machine learning artifact; andtransmit, over a network, output of the machine learning artifact to anexternal UX analysis service, wherein the output of the machine learningartifact is used by the external UX analysis service to trigger a rewardto an account associated with the user.
 20. The computer-readable mediumof claim 16, wherein the instructions when executed by the processor,further cause the processor to: acquire, via one or more accelerometersof the computing device, one or more detected accelerometer signalsimmediately following and/or during the audio output as an accelerometerfile or as cached data, wherein the one or more detected accelerometersignals comprise an additional part of the user-experience (UX) feedbackof the user-interface presentation; and determine a second matrixcomprising the plurality of acquired accelerometer signals and providingthe second matrix to the machine learning artifact, wherein the secondmatrix is used as a second input to the machine learning artifactconfigured with weights specific to the multimedia output to evaluateand quantify a user-experience (UX) feedback to the multimedia output.